Currently, Analytics as a Service (AaaS) represents one of the fastest evolving services on the cloud, normally embodied by a cloud platform that provides analytics. Usually, in most services, such analytics are performed by classifiers, that is, components or modules configured to undertake data analysis via an analytics model.
A recurrent problem involves the on-boarding of many analytical services onto the cloud. Normally, a customer provides data on which analytics are then performed on the cloud, based on an already existing analytics framework (or model) at a classifier. These models are typically configured for a highly specified use, built on the basis of predetermined first data sets. In other words, the models tend to assume the use of specific types or features of data and do not prove to be highly flexible or versatile.
Generally, analysis based on a model built from a training set assumes that subsequent incoming data sets will conform to the parameters of the training set. In a multi-tenant setting, a cloud provider even needs to tailor its analytics (and classifiers) to meet the data availability of each tenant even for the same analytics.
A challenge thus emerges when data sets evolve, even slightly, as conventional analytics models on the cloud may not be readily configured to adapt to such changes. Accordingly, there is little recourse short of retraining an entire model. Beyond the time involved, domain expertise may well be required to assist in such retraining, leading to a potentially cumbersome and expensive process.